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Test Info: Warnings
- This test has a WPT meta file that expects 1 subtest issues.
- This WPT test may be referenced by the following Test IDs:
- /webnn/conformance_tests/softsign.https.any.html?cpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softsign.https.any.html?gpu - WPT Dashboard Interop Dashboard
- /webnn/conformance_tests/softsign.https.any.html?npu - WPT Dashboard Interop Dashboard
// META: title=test WebNN API softsign operation
// META: global=window
// META: variant=?cpu
// META: variant=?gpu
// META: variant=?npu
// META: script=../resources/utils.js
// META: timeout=long
'use strict';
// Compute the softsign function of the input tensor. The calculation follows
// the expression x / (1 + |x|).
//
// MLOperand softsign(MLOperand input);
const softsignTests = [
{
'name': 'softsign positive float32 1D constant tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
1.5834133625030518, 4.078719139099121, 8.883357048034668,
8.070859909057617, 8.211773872375488, 2.4554004669189453,
0.653374195098877, 7.866281032562256, 3.123955249786377,
8.013792037963867, 3.940986156463623, 1.813172698020935,
2.3906760215759277, 1.335968017578125, 9.416410446166992,
0.4432569146156311, 5.236661911010742, 9.42424201965332,
7.816190242767334, 5.849185943603516, 8.780370712280273,
5.120515823364258, 7.117222309112549, 4.599106788635254
],
'descriptor': {shape: [24], dataType: 'float32'},
'constant': true
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
0.6129152178764343, 0.8030999898910522, 0.8988198041915894,
0.8897568583488464, 0.8914432525634766, 0.7105979323387146,
0.3951762318611145, 0.8872131109237671, 0.7575143575668335,
0.8890588879585266, 0.7976112365722656, 0.6445294618606567,
0.7050735354423523, 0.5719119310379028, 0.9039976596832275,
0.30712267756462097, 0.8396578431129456, 0.9040697813034058,
0.8865723013877869, 0.8539972305297852, 0.8977543711662292,
0.8366150856018066, 0.8768051266670227, 0.8214001059532166
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'softsign positive float32 0D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [1.5834133625030518],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [0.6129152178764343],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'softsign negative float32 0D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [-2.597844123840332],
'descriptor': {shape: [], dataType: 'float32'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [-0.7220557928085327],
'descriptor': {shape: [], dataType: 'float32'}
}
}
}
},
{
'name': 'softsign positive float32 1D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
1.5834133625030518, 4.078719139099121, 8.883357048034668,
8.070859909057617, 8.211773872375488, 2.4554004669189453,
0.653374195098877, 7.866281032562256, 3.123955249786377,
8.013792037963867, 3.940986156463623, 1.813172698020935,
2.3906760215759277, 1.335968017578125, 9.416410446166992,
0.4432569146156311, 5.236661911010742, 9.42424201965332,
7.816190242767334, 5.849185943603516, 8.780370712280273,
5.120515823364258, 7.117222309112549, 4.599106788635254
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
0.6129152178764343, 0.8030999898910522, 0.8988198041915894,
0.8897568583488464, 0.8914432525634766, 0.7105979323387146,
0.3951762318611145, 0.8872131109237671, 0.7575143575668335,
0.8890588879585266, 0.7976112365722656, 0.6445294618606567,
0.7050735354423523, 0.5719119310379028, 0.9039976596832275,
0.30712267756462097, 0.8396578431129456, 0.9040697813034058,
0.8865723013877869, 0.8539972305297852, 0.8977543711662292,
0.8366150856018066, 0.8768051266670227, 0.8214001059532166
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'softsign negative float32 1D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-2.597844123840332, -0.4449555575847626, -9.095475196838379,
-3.7480077743530273, -1.3867290019989014, -8.220329284667969,
-3.538342237472534, -9.364588737487793, -6.283252239227295,
-5.002012252807617, -8.245729446411133, -3.775470495223999,
-4.087255001068115, -7.381676197052002, -5.8829216957092285,
-8.338910102844238, -6.60154914855957, -4.491941928863525,
-3.5247786045074463, -4.43991231918335, -5.234262466430664,
-1.5911732912063599, -9.106277465820312, -8.523774147033691
],
'descriptor': {shape: [24], dataType: 'float32'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.7220557928085327, -0.3079372048377991, -0.9009457230567932,
-0.7893853783607483, -0.5810165405273438, -0.891543984413147,
-0.7796552181243896, -0.9035176634788513, -0.8626986742019653,
-0.8333892226219177, -0.8918419480323792, -0.7905965447425842,
-0.8034303188323975, -0.8806921243667603, -0.8547128438949585,
-0.8929211497306824, -0.8684478402137756, -0.8179150223731995,
-0.7789947390556335, -0.8161734938621521, -0.8395960927009583,
-0.6140744686126709, -0.9010515809059143, -0.894999623298645
],
'descriptor': {shape: [24], dataType: 'float32'}
}
}
}
},
{
'name': 'softsign float32 2D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-8.343496322631836, -6.920152187347412, 2.699638843536377,
-8.663105010986328, -3.1905343532562256, 7.657886981964111,
6.650215148925781, 6.058011054992676, 0.6634320616722107,
5.8058037757873535, -0.32821124792099, 1.2704304456710815,
-9.946120262145996, 6.905375003814697, -0.031071536242961884,
-3.9696409702301025, 6.270823001861572, -2.639260768890381,
3.0513505935668945, 7.426476955413818, -8.454667091369629,
7.135868072509766, -4.986093997955322, -7.859614849090576
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.8929736614227295, -0.8737397789955139, 0.7297033667564392,
-0.8965135812759399, -0.7613669633865356, 0.8844983577728271,
0.8692847490310669, 0.8583170175552368, 0.3988332748413086,
0.8530665636062622, -0.24710771441459656, 0.5595548748970032,
-0.9086434245109558, 0.8735038042068481, -0.03013519011437893,
-0.798778235912323, 0.8624640107154846, -0.7252188920974731,
0.7531687617301941, 0.88132643699646, -0.8942321538925171,
0.8770874738693237, -0.8329461812973022, -0.8871282935142517
],
'descriptor': {shape: [4, 6], dataType: 'float32'}
}
}
}
},
{
'name': 'softsign float32 3D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-8.343496322631836, -6.920152187347412, 2.699638843536377,
-8.663105010986328, -3.1905343532562256, 7.657886981964111,
6.650215148925781, 6.058011054992676, 0.6634320616722107,
5.8058037757873535, -0.32821124792099, 1.2704304456710815,
-9.946120262145996, 6.905375003814697, -0.031071536242961884,
-3.9696409702301025, 6.270823001861572, -2.639260768890381,
3.0513505935668945, 7.426476955413818, -8.454667091369629,
7.135868072509766, -4.986093997955322, -7.859614849090576
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.8929736614227295, -0.8737397789955139, 0.7297033667564392,
-0.8965135812759399, -0.7613669633865356, 0.8844983577728271,
0.8692847490310669, 0.8583170175552368, 0.3988332748413086,
0.8530665636062622, -0.24710771441459656, 0.5595548748970032,
-0.9086434245109558, 0.8735038042068481, -0.03013519011437893,
-0.798778235912323, 0.8624640107154846, -0.7252188920974731,
0.7531687617301941, 0.88132643699646, -0.8942321538925171,
0.8770874738693237, -0.8329461812973022, -0.8871282935142517
],
'descriptor': {shape: [2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'softsign float32 4D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-8.343496322631836, -6.920152187347412, 2.699638843536377,
-8.663105010986328, -3.1905343532562256, 7.657886981964111,
6.650215148925781, 6.058011054992676, 0.6634320616722107,
5.8058037757873535, -0.32821124792099, 1.2704304456710815,
-9.946120262145996, 6.905375003814697, -0.031071536242961884,
-3.9696409702301025, 6.270823001861572, -2.639260768890381,
3.0513505935668945, 7.426476955413818, -8.454667091369629,
7.135868072509766, -4.986093997955322, -7.859614849090576
],
'descriptor': {shape: [1, 2, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.8929736614227295, -0.8737397789955139, 0.7297033667564392,
-0.8965135812759399, -0.7613669633865356, 0.8844983577728271,
0.8692847490310669, 0.8583170175552368, 0.3988332748413086,
0.8530665636062622, -0.24710771441459656, 0.5595548748970032,
-0.9086434245109558, 0.8735038042068481, -0.03013519011437893,
-0.798778235912323, 0.8624640107154846, -0.7252188920974731,
0.7531687617301941, 0.88132643699646, -0.8942321538925171,
0.8770874738693237, -0.8329461812973022, -0.8871282935142517
],
'descriptor': {shape: [1, 2, 3, 4], dataType: 'float32'}
}
}
}
},
{
'name': 'softsign float32 5D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-8.343496322631836, -6.920152187347412, 2.699638843536377,
-8.663105010986328, -3.1905343532562256, 7.657886981964111,
6.650215148925781, 6.058011054992676, 0.6634320616722107,
5.8058037757873535, -0.32821124792099, 1.2704304456710815,
-9.946120262145996, 6.905375003814697, -0.031071536242961884,
-3.9696409702301025, 6.270823001861572, -2.639260768890381,
3.0513505935668945, 7.426476955413818, -8.454667091369629,
7.135868072509766, -4.986093997955322, -7.859614849090576
],
'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float32'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.8929736614227295, -0.8737397789955139, 0.7297033667564392,
-0.8965135812759399, -0.7613669633865356, 0.8844983577728271,
0.8692847490310669, 0.8583170175552368, 0.3988332748413086,
0.8530665636062622, -0.24710771441459656, 0.5595548748970032,
-0.9086434245109558, 0.8735038042068481, -0.03013519011437893,
-0.798778235912323, 0.8624640107154846, -0.7252188920974731,
0.7531687617301941, 0.88132643699646, -0.8942321538925171,
0.8770874738693237, -0.8329461812973022, -0.8871282935142517
],
'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float32'}
}
}
}
},
// float16 tests
{
'name': 'softsign positive float16 1D constant tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
1.5830078125, 4.078125, 8.8828125, 8.0703125, 8.2109375,
2.455078125, 0.6533203125, 7.8671875, 3.123046875, 8.015625,
3.94140625, 1.8134765625, 2.390625, 1.3359375, 9.4140625,
0.443359375, 5.23828125, 9.421875, 7.81640625, 5.84765625,
8.78125, 5.12109375, 7.1171875, 4.59765625
],
'descriptor': {shape: [24], dataType: 'float16'},
'constant': true
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
0.61279296875, 0.80322265625, 0.89892578125, 0.8896484375,
0.8916015625, 0.71044921875, 0.395263671875, 0.88720703125,
0.75732421875, 0.88916015625, 0.7978515625, 0.64453125,
0.705078125, 0.57177734375, 0.90380859375, 0.30712890625,
0.83984375, 0.90380859375, 0.88671875, 0.85400390625,
0.89794921875, 0.83642578125, 0.876953125, 0.8212890625
],
'descriptor': {shape: [24], dataType: 'float16'}
}
}
}
},
{
'name': 'softsign positive float16 0D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [1.5830078125],
'descriptor': {shape: [], dataType: 'float16'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [0.61279296875],
'descriptor': {shape: [], dataType: 'float16'}
}
}
}
},
{
'name': 'softsign negative float16 0D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [-2.59765625],
'descriptor': {shape: [], dataType: 'float16'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [-0.72216796875],
'descriptor': {shape: [], dataType: 'float16'}
}
}
}
},
{
'name': 'softsign positive float16 1D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
1.5830078125, 4.078125, 8.8828125, 8.0703125, 8.2109375,
2.455078125, 0.6533203125, 7.8671875, 3.123046875, 8.015625,
3.94140625, 1.8134765625, 2.390625, 1.3359375, 9.4140625,
0.443359375, 5.23828125, 9.421875, 7.81640625, 5.84765625,
8.78125, 5.12109375, 7.1171875, 4.59765625
],
'descriptor': {shape: [24], dataType: 'float16'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
0.61279296875, 0.80322265625, 0.89892578125, 0.8896484375,
0.8916015625, 0.71044921875, 0.395263671875, 0.88720703125,
0.75732421875, 0.88916015625, 0.7978515625, 0.64453125,
0.705078125, 0.57177734375, 0.90380859375, 0.30712890625,
0.83984375, 0.90380859375, 0.88671875, 0.85400390625,
0.89794921875, 0.83642578125, 0.876953125, 0.8212890625
],
'descriptor': {shape: [24], dataType: 'float16'}
}
}
}
},
{
'name': 'softsign negative float16 1D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-2.59765625, -0.445068359375, -9.09375, -3.748046875, -1.38671875,
-8.21875, -3.5390625, -9.3671875, -6.28515625, -5.00390625,
-8.2421875, -3.775390625, -4.0859375, -7.3828125, -5.8828125,
-8.3359375, -6.6015625, -4.4921875, -3.525390625, -4.44140625,
-5.234375, -1.5908203125, -9.109375, -8.5234375
],
'descriptor': {shape: [24], dataType: 'float16'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.72216796875, -0.30810546875, -0.90087890625, -0.78955078125,
-0.5810546875, -0.8916015625, -0.77978515625, -0.9033203125,
-0.86279296875, -0.83349609375, -0.8916015625, -0.79052734375,
-0.80322265625, -0.880859375, -0.8544921875, -0.89306640625,
-0.86865234375, -0.81787109375, -0.77880859375, -0.81640625,
-0.83935546875, -0.6142578125, -0.90087890625, -0.89501953125
],
'descriptor': {shape: [24], dataType: 'float16'}
}
}
}
},
{
'name': 'softsign float16 2D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-8.34375, -6.921875, 2.69921875, -8.6640625,
-3.19140625, 7.65625, 6.6484375, 6.05859375,
0.66357421875, 5.8046875, -0.328125, 1.2705078125,
-9.9453125, 6.90625, -0.03106689453125, -3.96875,
6.26953125, -2.638671875, 3.05078125, 7.42578125,
-8.453125, 7.13671875, -4.984375, -7.859375
],
'descriptor': {shape: [4, 6], dataType: 'float16'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.89306640625, -0.87353515625, 0.7294921875,
-0.896484375, -0.76123046875, 0.88427734375,
0.869140625, 0.8583984375, 0.39892578125,
0.85302734375, -0.2470703125, 0.5595703125,
-0.90869140625, 0.87353515625, -0.0301361083984375,
-0.798828125, 0.8623046875, -0.72509765625,
0.7529296875, 0.88134765625, -0.89404296875,
0.876953125, -0.8330078125, -0.88720703125
],
'descriptor': {shape: [4, 6], dataType: 'float16'}
}
}
}
},
{
'name': 'softsign float16 3D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-8.34375, -6.921875, 2.69921875, -8.6640625,
-3.19140625, 7.65625, 6.6484375, 6.05859375,
0.66357421875, 5.8046875, -0.328125, 1.2705078125,
-9.9453125, 6.90625, -0.03106689453125, -3.96875,
6.26953125, -2.638671875, 3.05078125, 7.42578125,
-8.453125, 7.13671875, -4.984375, -7.859375
],
'descriptor': {shape: [2, 3, 4], dataType: 'float16'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.89306640625, -0.87353515625, 0.7294921875,
-0.896484375, -0.76123046875, 0.88427734375,
0.869140625, 0.8583984375, 0.39892578125,
0.85302734375, -0.2470703125, 0.5595703125,
-0.90869140625, 0.87353515625, -0.0301361083984375,
-0.798828125, 0.8623046875, -0.72509765625,
0.7529296875, 0.88134765625, -0.89404296875,
0.876953125, -0.8330078125, -0.88720703125
],
'descriptor': {shape: [2, 3, 4], dataType: 'float16'}
}
}
}
},
{
'name': 'softsign float16 4D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-8.34375, -6.921875, 2.69921875, -8.6640625,
-3.19140625, 7.65625, 6.6484375, 6.05859375,
0.66357421875, 5.8046875, -0.328125, 1.2705078125,
-9.9453125, 6.90625, -0.03106689453125, -3.96875,
6.26953125, -2.638671875, 3.05078125, 7.42578125,
-8.453125, 7.13671875, -4.984375, -7.859375
],
'descriptor': {shape: [1, 2, 3, 4], dataType: 'float16'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.89306640625, -0.87353515625, 0.7294921875,
-0.896484375, -0.76123046875, 0.88427734375,
0.869140625, 0.8583984375, 0.39892578125,
0.85302734375, -0.2470703125, 0.5595703125,
-0.90869140625, 0.87353515625, -0.0301361083984375,
-0.798828125, 0.8623046875, -0.72509765625,
0.7529296875, 0.88134765625, -0.89404296875,
0.876953125, -0.8330078125, -0.88720703125
],
'descriptor': {shape: [1, 2, 3, 4], dataType: 'float16'}
}
}
}
},
{
'name': 'softsign float16 5D tensor',
'graph': {
'inputs': {
'softsignInput': {
'data': [
-8.34375, -6.921875, 2.69921875, -8.6640625,
-3.19140625, 7.65625, 6.6484375, 6.05859375,
0.66357421875, 5.8046875, -0.328125, 1.2705078125,
-9.9453125, 6.90625, -0.03106689453125, -3.96875,
6.26953125, -2.638671875, 3.05078125, 7.42578125,
-8.453125, 7.13671875, -4.984375, -7.859375
],
'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float16'}
}
},
'operators': [{
'name': 'softsign',
'arguments': [{'input': 'softsignInput'}],
'outputs': 'softsignOutput'
}],
'expectedOutputs': {
'softsignOutput': {
'data': [
-0.89306640625, -0.87353515625, 0.7294921875,
-0.896484375, -0.76123046875, 0.88427734375,
0.869140625, 0.8583984375, 0.39892578125,
0.85302734375, -0.2470703125, 0.5595703125,
-0.90869140625, 0.87353515625, -0.0301361083984375,
-0.798828125, 0.8623046875, -0.72509765625,
0.7529296875, 0.88134765625, -0.89404296875,
0.876953125, -0.8330078125, -0.88720703125
],
'descriptor': {shape: [1, 2, 1, 3, 4], dataType: 'float16'}
}
}
}
}
];
if (navigator.ml) {
softsignTests.forEach((test) => {
webnn_conformance_test(buildAndExecuteGraph, getPrecisionTolerance, test);
});
} else {
test(() => assert_implements(navigator.ml, 'missing navigator.ml'));
}